Module code: SOCM052

Module Overview

Simulating social interactions in virtual research labs using agent-based modelling is increasingly allowing researchers to gain new insights into the complex ways that individuals and societies function. 

In this module, students will be introduced to foundational theoretical and practical aspects of this approach. The module covers the process of agent-based modelling, from conceptualising a research question, where to obtain data, operationalisation and formalisation of data, model implementation, and model analysis and interpretation. In addition to the theoretical content, the student will learn NetLogo as a programming language for agent-based models. On the basis of a detailed model of a social phenomenon (e.g. a market, virus spread) that is developed step-by-step in lab sessions, the major features of programming in NetLogo are learned through practical application. Through this guided implementation the student will acquire basic to intermediate programming skills in NetLogo as well as engaging with the step-by-step development of a model. 

Module provider


Module Leader

ELSENBROICH Corinna (Sociology)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

Module cap (Maximum number of students): N/A

Overall student workload

Workshop Hours: 4

Independent Learning Hours: 126

Lecture Hours: 10

Laboratory Hours: 10

Module Availability

Semester 2

Prerequisites / Co-requisites


Module content

Indicative content includes: 

  • What is agent-based modelling

  • Basics of agent-based model implementations

  • Approaches to behaviour rules (eg. game theory, BDI, social psychology)

  • Running and analysing experiments

  • Sensitivity analysis and robustness tests

  • Verification and validation

  • Intermediate NetLogo

Assessment pattern

Assessment type Unit of assessment Weighting
Coursework Implemented model 40
Coursework Model analysis (2000 words) 60

Alternative Assessment


Assessment Strategy

The assessment strategy is designed to: 

Provide students with the opportunity to demonstrate their knowledge, analytical capacity and practical skills of agent-based modelling. The dual learning outcome of the course (theoretical and practical), are mirrored in the assessment strategy consisting of an implemented model and a theoretical/critical essay engaging with the model and the relevant literature. The model is a practical implementation of a social phenomenon, e.g. an extension of an existing model. The essay is a report on the model design and implementation, a positioning of the model in relevant literature and an analysis of the results.

Thus, the summative assessment for this module consists of:

An implemented model (40%): The model could be an extension of a classic model (e.g. implementing new migration regimes into a segregation model) or an idea developed independently by the student. The model needs to be accompanied by a description of its domain of application and research question, a description of how it works, what can be investigated with it and initial results. 

A 2000 word model analysis (60%): The analysis is a critical engagement with the model built in the previous assignment. It will contain a literature review, position the model within the relevant literature and analyse, describe and interpret the results of the model. 

Formative assessment and feedback

Formative assessment will be provided in the lab sessions and individual supervision. The lab sessions are focussed on learning programming in NetLogo and in doing so will provide the student with immediate feedback and help to develop their programming skills. Students are invited to discuss their conceptual models in one-to-one sessions to get feedback on feasibility and scope. The essay assignment has written feedback and students are invited to one-to-one sessions to discuss this feedback and how to learn from it for future assignments.

Module aims

  • • To understand basic features of social simulation modelling in the social sciences
  • • To be able to think about a social problem in an agent-based modelling relevant way
  • • To understand particular features of modelling social phenomena, e.g. networks, neighbourhoods, social influence
  • • To be able to conceptualise different kinds of agents, e.g. behavioural, reactive, cognitive
  • • To be able to engage in the research process of modelling including model conception, specification, implementation, verification and validation
  • • To learn programming in NetLogo to an intermediate level

Learning outcomes

Attributes Developed
001 Understand the foundations of social simulation K
002 Understand application areas of agent-based modelling KC
003 Understand different implementations of social phenomena KC
004 Be able to program in NetLogo KPT
005 Be able to provide a basic model specification and a basic implementation P

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

Methods of Teaching / Learning

The learning and teaching strategy is designed to integrate theoretical knowledge of social simulation with practical skills for the implementation of simulation models. The lectures provide theoretical content, the lab sessions hone programming skills and facilitate the application of theoretical understanding to model building. 

The learning and teaching methods include:

  • Lectures

  • Practical workshops

  • Group discussion 

This module is taught intensively during one-week. Days 1-3 will consist of a combination of lectures and hands on practical sessions using NetLogo as a programming language. Day 4 is devoted to independent study, allowing students to undertake preparatory work on their assignment. Finally, on day 5 students will get the opportunity to receive formative feedback on their initial assignment plans and peer feedback during group discussion. Students will then complete their practical assignment.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list
Upon accessing the reading list, please search for the module using the module code: SOCM052

Programmes this module appears in

Programme Semester Classification Qualifying conditions
Social Research Methods MSc 2 Optional A weighted aggregate mark of 50% is required to pass the module

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2020/1 academic year.